HI6008 Business Research: Big Data in Organizations Report

This report investigates the impact of big data on organizational operations, focusing on its role in achieving business transformation and competitive advantage. It begins with a topic selection and justification for research questions, highlighting the importance of technological intervention and digitalization. The methodology section details the use of both quantitative and qualitative data collection methods, including surveys and interviews, along with the formulation of a hypothesis. The report further explores the use of secondary data, including charts, tables, and graphs. The report examines questionnaire design factors and presents a summary of findings, emphasizing big data's role in maintaining a competitive edge and driving innovation. The report also analyzes different analytical processes, data mining, and decision-making approaches. Finally, the report discusses the benefits of big data, such as reduced operational costs, and the importance of effective communication and risk assessment.

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Big data in organizations
Name of the student:
Name of the university:
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Table of contents
Introduction................................................................................................................................2
Topic selection.......................................................................................................................2
Justification for the development of research questions........................................................2
Methodology..............................................................................................................................3
Quantitative-qualitative debate..............................................................................................3
Hypothesis..............................................................................................................................3
Additional secondary data......................................................................................................3
Proposed primary data sample size........................................................................................4
Primary data sampling approach............................................................................................4
Questionnaire design factors..................................................................................................4
Summary of the findings............................................................................................................6
Expectations.........................................................................................................................10
Conclusion................................................................................................................................11
References................................................................................................................................13

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Introduction
Topic selection
In the current business scenario, big data is an effective option for achieving radical
transformation of the operations. As a matter of specification, the topic has been selected for
assessing the role of information technology on the efficient execution of the operations.
Technological intervention is accounted as a primary source for achieving economic growth
(Fan, Lau and Zhao 2015). Businesses have adopted continuous improvement strategies for
thinking innovative solutions to improve the standards and quality of the processes. Digital
platforms have assisted the businesses to enhance the efficiency with the activities of
accumulating, processing and utilization of bulk data.
Big data has assisted the companies and organizations to digitalize the operations.
This approach is effective in terms of competing with the contemporary brands in the
achievement of customer satisfaction through the provision of quality products and services.
Systematic approach in this context is beneficial in terms of attaining higher competitive
advantage over the contemporaries (Loebbecke and Picot 2015). One of the other reasons for
selection of the topic of Big Data is to speculate the parameter of governance through the
means of incorporating the relevant regulatory mechanisms.
Justification for the development of research questions
Research questions help the researcher to excavate different parameters related to the
subject matter. In the current context, the researcher has considered the parameters of
application benefits, disadvantage and future implications for shaping the effective of Big
Data in the workplace conditions and issues. These parameters work up collaboratively to
enhance the organizational effectiveness. The researcher has developed the research
questions bearing in mind these parameters and their importance in the improving the

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organizational attractiveness (Shannon-Baker 2016). Moreover, it can also be said that these
questions are assistance for the researcher to develop crucial linkages between the variables.
Methodology
Quantitative-qualitative debate
For conducting this research, both quantitative and qualitative means of data
collection would be used. In case of quantitative data collection methods, survey would be
conducted on 50 employees, who are working full time in reputed companies and
organizations. On the other hand, in the qualitative method, 3 managers would be interviewed
on the steps taken for implementing Big Data in the workplace. Apart from this, Edmonds
and Kennedy (2016) is of the view that relevant themes would be developed for analysing the
impact of Big Data application on the productivity of the companies and organizations.
Hypothesis
H0- Big Data does not have any impact on the enhancement of the organizational
attractiveness
H1- Big Data creates heavy impact on the enhancement of the organizational attractiveness
Conceptual framework is one of the agents used by the researcher for adding an
interrogative parameter to the subject matter. This relation is assistance in terms of
excavating the positive and the negative aspects related to the implementation of Big Data
Analytics in the organizations.
Additional secondary data
In addition to the surveys and interviews, thematic analysis would be conducted for
mapping the impact of the Big Data on the business processes. In this context, mention can be
made of the consideration of charts, tables, graphs and statistical data for gaining an insight
into the extent to which Big data has influenced the operations of the organization
(Archibald, 2016). This tools would be assistance for the researcher to attain justification for

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the propositions and assumptions by linking with the theories and models. This linkage,
further, is assistance for linking the theories and models with the previous researches on the
utility of big data on enhancing the standards and quality of the organizational processes. It
can also be deduced that this linkage is apt for proposing recommendations, which establishes
essential linkages between the propositions and the developed aims and objectives. In this
context, Turner, Cardinal and Burton (2017) argues that hypothesis is also linked in terms of
assessing the positive and the negative parameters and achieving justifications.
Proposed primary data sample size
In this research, the proposed sample size is 50 employees and 3 managers.
Primary data sampling approach
The employees would be selected on simple random technique for coping up with the
time and financial constraints. The managers would be selected on the basis of their
experience and tenurity in the organization. According to the arguments of McKim (2017),
this technique would be apt for mapping their approach towards the utility of big data and
their application into the execution of the business processes.
Questionnaire design factors
For conducting research on the implications of Big Data, survey questionnaires would
be prepared. In this survey, the questions would relate to the use of the Big Data in terms of
storing and processing the information. Apart from this, McCusker and Gunaydin (2015)
states that the questions would revolve around the challenges, which obstructs the data
processing activities in the organizations. The following are the sample questions:
Q1. Are you aware of the Big Data Analytics, which have been implemented in the
organizations?
 Yes
 No

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Q2. How far do you think that Big Data has transformed the nature of the workplace
operations?
 Strongly Agree
 Agree
 Neutral
 Disagree
 Strongly Disagree
Q3. What are the challenges, which you have faced in using the Big Data analytics?
 Interrupted internet connections
 Cyber crimes
 Synchronization across the disparate data sources
 Shortage of skilled professionals
 Bulk data
Q4. What are the strategies used for using the Big Data Analytics for getting the
maximum benefits?
 Developing smart grid
 Adopting protective maintenance measures
 Data exploration
 Social analytics
 Decision Science
Q5. How far do you think that change management theories and models would be effective
for transforming the standards and quality of the operations?
 Strongly Agree
 Agree

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 Neutral
 Disagree
 Strongly Disagree
Q6. What do you think about the acceptance level of the Big Data in terms of standardizing
the business processes of IT companies?
 Strongly agree
 Agree
 Neutral
 Disagree
 Strongly Disagree
Q7. What recommendations do you suggest for enhancing the utility of big data in the
organizations?
 Developing customer centric outcomes
 Strategy for standardizing the enterprise operations
 Mapping the accessibility of the available data
 Identification of the priorities and building strategies
 Consideration of the business cases for measuring the outcomes
Summary of the findings
Big Data plays an important role in the present business context for maintaining the
competitive pace and sustaining the market position. Data driven strategies are crucial in
terms of dealing with the competitive rivalry. In this context, the essential aspects are
innovation, capturing and competing. Previous researches highlight the transformation of the
businesses in the sectors of healthcare to that of IT (Popovič et al. 2018). As a matter of

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specification, Big Data has enabled the staffs to standardize the operations according to the
needs, demands and requirements of the customers. Mapping the responses through the
means of feedbacks and survey is essential in terms of assessing the trends in the field of Big
Data.
Use of the advanced software is one of the typical exemplars of the big data analytics
in a broader perspective. One of the benefits in this context is the ease in coping up with the
time constraints, which is beneficial in terms of undertaking relevant decisions. Various
analytical process are applied for enhancing the efficiency in the processes. Agile working
practices proves effective for maintaining the competitive pace of the market (Williams
2016). Low cost of the software is assistance in terms of maintaining the balance in the
supply and demand factors.
Confirmatory Data Analysis is one of the essential concepts, which adds accuracy to
the operations. Conventional Statistical tools are fruitful in terms of analysing the inference
and its feasibility in the current workplace conditions. This analysis is also beneficial for
addressing the challenges and risks, which act as a major obstacle for executing the processes
according to the plans are requirements. The techniques, which are used in this context is data
processing, which is assistance in mapping the flow of information from the internal to the
external environment. Along with this, Wang et al. (2016) proposes that the techniques also
include hypothesis testing, variation and regression analysis and others.
Grounded theory of analysis is eccentric approach towards for data collection in an
efficient and effective manner. Revelation of the arguments of the analysts is apt for aligning
the responses with that of the hypothetical parameter. Data mining is one of the related
activities, which helps in assessment of the information at every stage. This process is
beneficial in terms of deducing new insights. Beliefs regarding the adequacy of data

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collection is not always appropriate, as there are exception, altering the aims and objectives.
Typical evidence of this lies in the performance exposed in preparation of the reports and
dashboards. After the collection of the satisfactory data, grounded theory approach initiates,
which is apt in terms of gaining justification for the propositions (Sun, Sun and Strang 2018).
This approach is exhaustive due to the application of indexation and exploration of the
unexplored realms of the data processing. Series of experiments are conducted for developing
the trends and ideas related to the data application and utility. Mention can be made of the
trial and error methods, which generates brainstorming ideas within the thinking horizons of
the employees.
Repetation of the process seems effective for deducing new insights in the stages of
data collection and assessment. Specific mention can be made of the decision making tree,
which helps in eliminating the irrelevant issues and aspects. This approach seems to be
effective for reaching to the appropriate decision, which can be applied for deducing relevant
conclusions and recommendations. Applying consistent modifications alters the traditional
nature of the research, however, it is fruitful in terms of achieving the correct and the
appropriate conclusion (Akter and Wamba 2016).
One of the benefits achieved from the use of Big Data is the reduced operational cost.
Incurred cost along with the maintenance cost is also lower, which is advantageous in terms
of setting the prices of the products and the services. The process in which the data is
gathered is also accounted as important in terms of gathering essential data and processing it
for enhancing the ease of the clients and the customers. Using different decision making
predictions helps in incorporating the effective decisions towards processing of the data and
information. Effective communication processes are necessary in terms of disseminating the
correct data to the clients and the customers so that they get the basic and the fundamental
concepts regarding the Big Data and its utility in the processes (Wang, Kung and Byrd 2018).

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Offshore management is one of effective techniques, supporting the processing of the
empathic data for addressing the specific needs, demands and requirements of the clients and
the customers. Extensive data collection and analysis is assistance in terms of delving deeper
into the mechanisms related to the applicability of the big data. This communication process
is assistance in terms of expanding the scope and arena of the supply chain network. Records
of the detailed profile of the clients and the customers is assistance in terms of averting the
instances of cybercrime. This is through the means of adopting security cookies and policies
for enhancing the privacy matters and issues.
Communication with the clients and the customers during the actual time of executing
the processes help in mapping the thought process. According to Ji-fan et al. (2017),
monitoring schemes helps in estimating the scope and arena to which the data processing can
be applied for expanding the scope and arena of the business mechanisms. In this context,
risk assessment is an essential aspect in terms of detecting the areas in which modifications
need to be applied for altering the standards and quality of the processes. Using risk
assessment matrix is beneficial in terms of planning the mitigation strategies for extracting
maximum benefits. Insights can be drawn into the channels of Facebook and Yahoo, where
the cases of data breaching are common. These cases act as a lesson for the companies and
organizations in terms of using the big data rationally for gaining the maximum benefits. In
this context, Sun, Zou and Strang (2015) is of the view that regulations like that of Data
Protection Act are fruitful in terms of averting the scandals of cybercrimes and data leakages.
The business are required to comply with the latest standards of the data protection. In
this context, mention can be made of the challenges in improper scalability of the
infrastructure, which obstructs the process of the data processing. In this case, IBM,
Facebook and Yamaha are the exceptions as they have adopted Big data for digitalization into
the services and operations.

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Expectations
Machine learning is expected to create wonders in the field of information
technology. As a matter of specification, consideration of the latest and modern machines
would act assistance in adding automation into the services. Akter et al. (2016) argues that
the modern software would enable the staffs to safely secure the information in the zipped
folders for processing it to the clients and the customers. Training the staffs about the use of
the big data analytics would be a planned and reasoned action in terms of achieving
maximum benefits. In this context, supervision of the Chief Data Manager is needed to
ensure that the relevant aspects of big data are disseminated to the employees. Within the
training courses, transaction of the algorithm needs to be incorporated for enhancing the
awareness of the employees about the mechanisms.
If the concept of stream of influences is considered, then it would cover the aspects of
customer retention through the means of acquisition. In this context, Grover et al. (2018) is of
the view that the employees needs to be made aware that customers are the vital source,
through which sales revenue and profit margin can be enhanced. In order to achieve the
maximum benefit, data strategy needs to be developed for enhancing the efficiency in the
operations. Within this, mention can be made of the problem solving cycle, which would help
the brand to conduct promotional campaigns. Brands like Netflix needs to be considered for
increasing the number of subscribers from which the scope and arena of the data collection
can be expanded.
Big Data analytics can be an effective source for boosting the sales revenue. For this,
the strategy of product development needs to be adopted through the means of innovation.
Gandomi and Haider (2015) proposes that saving the latest designs developed in the zipped
folders through cloud computing can be advantageous in terms of luring large number of
customers. Leveraging big data can also act assistance in expanding the market share. For

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this, conducting value chain analysis can be an effective option. Reference can be cited of the
data driven logistics and statistics, which would enhance the awareness about the
digitalization and its role in enhancing the organizational effectiveness.
Big Data analytics can also be linked to supply chain management. As a matter of
specification, adopting the social media marketing would be effective for enhancing the data
flexibility through systems integration. On the contrary, Wamba et al. (2017) is of the view
that systematic approach in this context is reflected from the application of logical reasoning,
which adds reliability and authenticity to the processing of data. Seminars and lectures can be
conducted for upgrading the knowledge, skills and expertise of the suppliers, especially
contextual intelligence. Mention can be made of the management of bulk information, which
are stored. Proper strategies can be developed for handling the data according to the
requirements and plans.
Along with this, the training and the development programs need to include sessions
on inventory warehouse and POS inventory, reconciling, which would be assistance in terms
of estimating and forecasting the shipment of the products according to the needs, demands
and requirements of the clients and the customers. Loebbecke and Picot (2015) opines that
Big data analytics can be one of the effective options for diversifying the business operations.
Divisional structure needs to be practiced for enhancing the collaboration between the
departmental units. Web based applications prove beneficial in terms of enhancing the
efficiency in the operations. The crucial components in this contexts would be social
computing, documentation of the internal texts, search index, and others.
Conclusion
Big Data seems to be an effective option for the business in terms of adding
digitalization into the business operations. Development of effective strategies is needed in
terms of achieving the maximum benefits. Adoption of latest and modern software is

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assistance in terms of automatically transferring the data from one source to the other. Within
the process, consciousness is needed towards the data leakages, which can stall the
productivity. Therefore, security cookies and policies are crucial for securing the information
stored in the folders. Training and development programs including inventory warehouse and
POS inventory, reconciling would enhance the awareness about the latest trends in the field
of information technology.

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References
Akter, S. and Wamba, S.F., 2016. Big data analytics in E-commerce: a systematic review and
agenda for future research. Electronic Markets, 26(2), pp.173-194.
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J., 2016. How to improve
firm performance using big data analytics capability and business strategy alignment?.
International Journal of Production Economics, 182, pp.113-131.
Archibald, M.M., 2016. Investigator triangulation: A collaborative strategy with potential for
mixed methods research. Journal of Mixed Methods Research, 10(3), pp.228-250.
Edmonds, W.A. and Kennedy, T.D., 2016. An applied guide to research designs:
Quantitative, qualitative, and mixed methods. Sage Publications.
Fan, S., Lau, R.Y. and Zhao, J.L., 2015. Demystifying big data analytics for business
intelligence through the lens of marketing mix. Big Data Research, 2(1), pp.28-32.
Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and
analytics. International journal of information management, 35(2), pp.137-144.
Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value
from big data analytics: A research framework. Journal of Management Information Systems,
35(2), pp.388-423.
Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R. and Childe, S.J., 2017. Modelling
quality dynamics, business value and firm performance in a big data analytics environment.
International Journal of Production Research, 55(17), pp.5011-5026.
Loebbecke, C. and Picot, A., 2015. Reflections on societal and business model transformation
arising from digitization and big data analytics: A research agenda. The Journal of Strategic
Information Systems, 24(3), pp.149-157.

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McCusker, K. and Gunaydin, S., 2015. Research using qualitative, quantitative or mixed
methods and choice based on the research. Perfusion, 30(7), pp.537-542.
McKim, C.A., 2017. The value of mixed methods research: A mixed methods study. Journal
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Popovič, A., Hackney, R., Tassabehji, R. and Castelli, M., 2018. The impact of big data
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Shannon-Baker, P., 2016. Making paradigms meaningful in mixed methods research. Journal
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Turner, S.F., Cardinal, L.B. and Burton, R.M., 2017. Research design for mixed methods: A
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Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R. and Childe, S.J., 2017. Big
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International Journal of Production Economics, 176, pp.98-110.

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Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities
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Williams, S., 2016. Business intelligence strategy and big data analytics: a general
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Appendix 1